• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2016, Volume: 9, Issue: 30, Pages: 1-6

Original Article

A Novel Approach for Making Recommendation using Skyline Query based on user Location and Preference


Objectives: To propose a method to handle large number of user and to improve the accuracy and quality of recommendation system. Methods/Statistical Analysis: This paper presents an effective method to identify user location based on his/her preference using Skyline query outline Dominated object. Dominance object suggests that an object falls under good or better in all dimension or good at least one dimension. Skyline query using Recommendation system has increased in recent years. Skyline query using recommendation system mainly used location-based services to find the nearest location, based on user preference. Location-based Services are information services and have a number of uses in social networking. Location-based Service finds the nearest location based on user preferences but not provide location based on similarity and rating. So, the user is not satisfied by the given result. Findings: To resolve above problem, the collaborative filtering technique, K-nearest neighbor algorithm and Ranking Scheme being used by us. Using Collaborative filtering technique, we find the similarity and rating of an item. Using K-nearest neighbor approach finds the nearest distance of the similar item and ranking technique being used by us, to choose the most nearest location. In this paper we take temporary dataset and mathematically evaluate our proposed system. Application/Improvements: In future, we will develop web tool which identify location and display result on map. We will also check user s’ past movement history based on content based recommendation system. Skyline query using recommendation system is use various domain i.e. House Rent/buying, travel and tourism business.
Keywords: Collaborative Filtering Technique, Dominated Object, K-Nearest Neighbor, Recommendation System, Skyline Query


Subscribe now for latest articles and news.